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•The aimof GRASPis the design of a cognitive system capable of performing grasping and manipulationtasks in open-ended environments, dealing with novelty, uncertainty and unforeseen situations.

•To meet the aim of the project, studying the problem of object manipulation and grasping will providea theoretical and measurable basis for system design that is valid in both human and artificial systems.

•To demonstrate the feasibility of our approach, we will instantiate, implement and evaluate ourtheories and hypotheses on robot systems with different embodiments and complexity.

•GRASP goes beyond the classical perceive-act or act-perceive approach and implements a predict-act-perceive paradigm that originates from findings of human brain research and results of mental trainingin humans where the self-knowledge is retrieved through different emulation principles.

•The knowledge of grasping in humans can be used to provide the initial model of the grasping processthat then has to be grounded through introspection to the specific embodiment.

•To achieve open-ended cognitive behaviour, we use surprise to steer the generation of graspingknowledge and modeling.

1.Theory of Grasp Modeling

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We study the requirements and effects of the agent's embodimenton the situatedness, awareness, task and environment understanding and thus provide the meansfor adaptation and self-reasoning.

2.Self-

and Context-Awareness

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We investigate how an agent benefits from using tutor basedand autonomous exploration together with physical modeling of the world to learn more aboutthe possibilities and constraints offered by its embodiment.

3.Curiosity and Surprise Driven Behaviour

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Wewillshow how the detection of an unexpectedevent or action is exploited to efficiently add new values, categories or dimensions to the graspingontology while at the same time exploiting surprise to bootstrap the learning process.Expectations are derived from the prediction of agent behaviour using the experiences gainedfrom self-awareness or introspection.

4.Inferring new Grasping Strategies

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We will use the ontology and acquired generalknowledge to generate expectations for grasping and manipulation tasks as means of correctionbetween the predicted and the actual state. This will allow adaptation to new objects andsituations without the need for extensive re-programming.

5.Exploitation to Future Prosthesis, Industrial and Service Markets

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We set out to exploitthe theoretical findings by investigating the grasp mapping to different artificial hands. Theobjective is to learn how kinematical design and the number of DOFs influence dexterity and howto optimize the graspable sub-set of all possible grasps while minimizing DOFs.

1.Learning to Observe Human Graspingand Consequences of Grasping

2.Representations and Ontology forLearning and Abstraction of Grasping